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<p>With the rapid development of the cryptocurrency market, price volatility and liquidity have become key indicators for understanding market behavior. However, their joint dynamics are often shaped by higher-order, multi-attribute interactions that are not well captured by conventional low-order analyses. Existing approaches largely emphasize pairwise relationships and thus struggle to provide a system-level and time-varying characterization of whether multiple market attributes convey redundant overlap or synergistic complementarity, leaving an important gap in understanding multivariate market organization. This paper proposes a multi-scale network framework based on information dynamics, built on O-information theory and quantified through three parallel measures: global O-information rate (global OIR), local OIR, and the OIR gradient, to characterize redundancy–synergy structures in multi-attribute cryptocurrency systems. Using Binance Vision spot-market data for twelve major cryptocurrencies from August 1, 2020 to November 30, 2025 across four sampling intervals (2&#xa0;h, 6&#xa0;h, 12&#xa0;h, and 1d), we conduct rolling-window estimation and systematic cross-scale comparisons. The results reveal pronounced cross-asset heterogeneity and support a distinction between leading and non-leading assets, with BTC, ETH, XRP, and BNB identified as leading assets while also exhibiting within-group differences. The findings provide a holistic and dynamic perspective on higher-order information organization in digital-asset markets and suggest that HOI-based indicators offer information that complements conventional connectedness measures in financial research.</p>

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A multi-scale network framework in digital asset markets based on high-order information dynamics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月5日

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  • Conghan Xiao,
  • Ke Chen

摘要

With the rapid development of the cryptocurrency market, price volatility and liquidity have become key indicators for understanding market behavior. However, their joint dynamics are often shaped by higher-order, multi-attribute interactions that are not well captured by conventional low-order analyses. Existing approaches largely emphasize pairwise relationships and thus struggle to provide a system-level and time-varying characterization of whether multiple market attributes convey redundant overlap or synergistic complementarity, leaving an important gap in understanding multivariate market organization. This paper proposes a multi-scale network framework based on information dynamics, built on O-information theory and quantified through three parallel measures: global O-information rate (global OIR), local OIR, and the OIR gradient, to characterize redundancy–synergy structures in multi-attribute cryptocurrency systems. Using Binance Vision spot-market data for twelve major cryptocurrencies from August 1, 2020 to November 30, 2025 across four sampling intervals (2 h, 6 h, 12 h, and 1d), we conduct rolling-window estimation and systematic cross-scale comparisons. The results reveal pronounced cross-asset heterogeneity and support a distinction between leading and non-leading assets, with BTC, ETH, XRP, and BNB identified as leading assets while also exhibiting within-group differences. The findings provide a holistic and dynamic perspective on higher-order information organization in digital-asset markets and suggest that HOI-based indicators offer information that complements conventional connectedness measures in financial research.